Stock Price Prediction Model Based on the Short-term Trending of KNN Method

2020 ◽  
Author(s):  
Sioiok Wong
Keyword(s):  
Prediction Model ◽  
Stock Price ◽  
Short Term ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Model Based

Stock Price Prediction Model Based on IWO Neural Network and its Applications

2014 ◽  
Vol 989-994 ◽  
pp. 1635-1640
Author(s):  
Hong Liu ◽  
Xiao Yan Lv
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Stock Price ◽  
Steepest Descent Method ◽  
Local Optimum ◽  
Back Propagation Algorithm ◽  
Feed Forward ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Model Based

In view of the deficiency of the standard back-propagation algorithm based on steepest descent method, a new kind of optimization strategy called invasive weed optimization (IWO) algorithm is introduced into the training process of feed-forward neural networks, and then a prediction model based on IWO feed-forward neural network (IWO-NN) is given. By the dynamic adjustment of standard deviation of the distribution of offspring individuals in IWO, the local convergence speed of networks is improved and the defect of trapping into a local optimum is reduced. By the empirical study of stock price prediction in Sany Heavy Industry, the results show that this method has better global astringency, robustness, and it is insensitive to initial values.

Stock Price Prediction Model Based on BA Neural Network and its Applications

2014 ◽  
Vol 989-994 ◽  
pp. 1646-1651 ◽  
Author(s):  
Xiao Yan Lv ◽  
Si Long Sun ◽  
Hong Liu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Stock Price ◽  
Frequency Tuning ◽  
Steepest Descent Method ◽  
Feed Forward ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Model Based ◽  
Intensive Exploitation

In view of the deficiency of the basic back-propagation (BP) algorithm based on steepest descent method. Bat algorithm (BA) in intelligent optimization is introduced into the training process of feed-forward neural networks, capturing the optimal solution of the objective function with a small population size and less number of iterations, and a prediction model based on BA feed-forward neural network (BA-NN) is given. By the empirical study of stock price prediction in Sany Heavy Industry, the results show that this method has advantages of frequency tuning and dynamic control of exploration and exploitation by automatic switching to intensive exploitation if necessary.

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